February 2026: Delivered Single-GPU Inference path for the ensemble model in ecmwf/anemoi-core. Added a conditional check for the grid_shard_shapes dataset selection during input assembly to enable inference on a single GPU with a trained ensemble model. This change fixes multi-dataset handling for AnemoiEnsModelEncProcDec (PR #852) and closes #851, aligning with contributor guidelines that unit tests, dependency updates, and documentation be included. Business impact includes reduced GPU usage, faster inference, and more flexible deployment for single-GPU environments. Collaboration with co-authors Ana Prieto Nemesio and Simon Lang; commits include 1327c8ce5a79003d170e4ef9c356b2775353659f. Technologies demonstrated: Python, dataset handling, ensemble-model workflows, and cross-functional collaboration.

January 2026: Stabilized the TruncatedConnection path in ecmwf/anemoi-core and added dynamic row normalization, delivering reliable initialization and on-the-fly normalization to speed training iterations and improve model performance across GPUs.

December 2025 (ecmwf/anemoi-core) monthly highlights: Key features delivered: - Robust Warm Start Path Handling: migrated warm_start_path to pathlib.Path for robust file path management and error handling across training workflows. Major bugs fixed: - Addressed fragile path handling that caused startup failures; improved error reporting and compatibility with multi-GPU parallel runs. Overall impact and accomplishments: - Significantly increased training workflow reliability and environment portability, reducing path-related failures and simplifying maintenance. - Documentation and testing emphasis aligned with project guidelines, enabling smoother future changes and parallel execution validation. - Collaboration reflected in PRs and co-authorship (Ana Prieto Nemesio and Harrison Cook). Technologies/skills demonstrated: - Python pathlib.Path usage, refactoring for robustness, and improved error handling. - Unit testing focus and multi-GPU validation readiness. - Cross-team collaboration and PR hygiene (co-authored changes).

Monthly performance summary for 2025-08 focusing on delivering business value and hardening the integration and prediction pipelines across two repositories. Key outcomes include enabling seamless AML MLflow logging in Anemoi via a NoAuth path and correcting a pre-processing invocation bug to stabilize predictions.

Monthly summary for 2025-07: Focused work on packaging and distribution readiness for ufs2arco in the conda-forge/staged-recipes repo, delivering a stable, cross-platform ready meta.yaml and related packaging hygiene that enables easier distribution and CI reliability.

Monthly work summary for 2024-11 focusing on delivering data access and ML enablement features, with clear business value for data workflows and reproducibility.